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Partial Discharges Monitoring for Electric Machines Diagnosis: A Review

Author

Listed:
  • Jonathan dos Santos Cruz

    (School of Electrical and Computer Engineering, University of Campinas, Campinas 13083-970, Brazil)

  • Fabiano Fruett

    (School of Electrical and Computer Engineering, University of Campinas, Campinas 13083-970, Brazil)

  • Renato da Rocha Lopes

    (School of Electrical and Computer Engineering, University of Campinas, Campinas 13083-970, Brazil)

  • Fabio Luiz Takaki

    (Eldorado Research Institute, Campinas 13083-898, Brazil)

  • Claudia de Andrade Tambascia

    (Eldorado Research Institute, Campinas 13083-898, Brazil)

  • Eduardo Rodrigues de Lima

    (Eldorado Research Institute, Campinas 13083-898, Brazil)

  • Mateus Giesbrecht

    (School of Electrical and Computer Engineering, University of Campinas, Campinas 13083-970, Brazil)

Abstract

Online monitoring of Partial Discharges (PDs) in rotating electrical machines is an useful tool for machine prognosis, as it presents reduced costs compared to intrusive inspections and is associated with relevant problems. Although this monitoring method has been developed for almost 50 years, the recent advancements in processes automation and signal processing techniques allow improvements that are still being studied by academic and industrial researchers. To analyze the current context of PDs monitoring, this article presents a literature review based on concepts of PDs in rotating machines, data acquisition techniques, state-of-the art commercial equipment, and recent methodologies for detection and pattern recognition of PDs. The challenges identified in the literature that motivate the development of more reliable and robust PD monitoring systems are presented and discussed.

Suggested Citation

  • Jonathan dos Santos Cruz & Fabiano Fruett & Renato da Rocha Lopes & Fabio Luiz Takaki & Claudia de Andrade Tambascia & Eduardo Rodrigues de Lima & Mateus Giesbrecht, 2022. "Partial Discharges Monitoring for Electric Machines Diagnosis: A Review," Energies, MDPI, vol. 15(21), pages 1-31, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:7966-:d:954621
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    References listed on IDEAS

    as
    1. Ana C. N. Pardauil & Thiago P. Nascimento & Marcelo R. S. Siqueira & Ubiratan H. Bezerra & Werbeston D. Oliveira, 2020. "Combined Approach Using Clustering-Random Forest to Evaluate Partial Discharge Patterns in Hydro Generators," Energies, MDPI, vol. 13(22), pages 1-18, November.
    2. Ramon C. F. Araújo & Rodrigo M. S. de Oliveira & Fernando S. Brasil & Fabrício J. B. Barros, 2021. "Novel Features and PRPD Image Denoising Method for Improved Single-Source Partial Discharges Classification in On-Line Hydro-Generators," Energies, MDPI, vol. 14(11), pages 1-33, June.
    3. Yuanlin Luo & Zhaohui Li & Hong Wang, 2017. "A Review of Online Partial Discharge Measurement of Large Generators," Energies, MDPI, vol. 10(11), pages 1-32, October.
    Full references (including those not matched with items on IDEAS)

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